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   "source": [
    "## Rollback\n",
    "\n",
    "This notebook assumes knowledge of what double-texting is, which you can learn about in the [double-texting conceptual guide](https://langchain-ai.github.io/langgraph/cloud/concepts/#double-texting).\n",
    "\n",
    "The guide covers the `rollback` option for double texting, which interrupts the prior run of the graph and starts a new one with the double-text. This option is very similar to the `interrupt` option, but in this case the first run is completely deleted from the database and cannot be restarted. Below is a quick example of using the `rollback` option.\n",
    "\n",
    "First, let's import our required packages and instantiate our client, assistant, and thread."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "\n",
    "import httpx\n",
    "from langchain_core.messages import convert_to_messages\n",
    "from langgraph_sdk import get_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = get_client()\n",
    "assistant_id = \"agent\"\n",
    "thread = await client.threads.create()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the first run will be interrupted\n",
    "rolled_back_run = await client.runs.create(\n",
    "    thread[\"thread_id\"],\n",
    "    assistant_id,\n",
    "    input={\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in sf?\"}]},\n",
    ")\n",
    "await asyncio.sleep(2)\n",
    "run = await client.runs.create(\n",
    "    thread[\"thread_id\"],\n",
    "    assistant_id,\n",
    "    input={\"messages\": [{\"role\": \"human\", \"content\": \"what's the weather in nyc?\"}]},\n",
    "    multitask_strategy=\"rollback\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wait until the second run completes\n",
    "await client.runs.join(thread[\"thread_id\"], run[\"run_id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the thread has data only from the second run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state = await client.threads.get_state(thread[\"thread_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "\n",
      "what's the weather in nyc?\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "[{'id': 'toolu_01JzPqefao1gxwajHQ3Yh3JD', 'input': {'query': 'weather in nyc'}, 'name': 'tavily_search_results_json', 'type': 'tool_use'}]\n",
      "Tool Calls:\n",
      "  tavily_search_results_json (toolu_01JzPqefao1gxwajHQ3Yh3JD)\n",
      " Call ID: toolu_01JzPqefao1gxwajHQ3Yh3JD\n",
      "  Args:\n",
      "    query: weather in nyc\n",
      "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
      "Name: tavily_search_results_json\n",
      "\n",
      "[{\"url\": \"https://www.weatherapi.com/\", \"content\": \"{'location': {'name': 'New York', 'region': 'New York', 'country': 'United States of America', 'lat': 40.71, 'lon': -74.01, 'tz_id': 'America/New_York', 'localtime_epoch': 1718734479, 'localtime': '2024-06-18 14:14'}, 'current': {'last_updated_epoch': 1718733600, 'last_updated': '2024-06-18 14:00', 'temp_c': 29.4, 'temp_f': 84.9, 'is_day': 1, 'condition': {'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}, 'wind_mph': 2.2, 'wind_kph': 3.6, 'wind_degree': 158, 'wind_dir': 'SSE', 'pressure_mb': 1025.0, 'pressure_in': 30.26, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 63, 'cloud': 0, 'feelslike_c': 31.3, 'feelslike_f': 88.3, 'windchill_c': 28.3, 'windchill_f': 82.9, 'heatindex_c': 29.6, 'heatindex_f': 85.3, 'dewpoint_c': 18.4, 'dewpoint_f': 65.2, 'vis_km': 16.0, 'vis_miles': 9.0, 'uv': 7.0, 'gust_mph': 16.5, 'gust_kph': 26.5}}\"}]\n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "The weather API results show that the current weather in New York City is sunny with a temperature of around 85°F (29°C). The wind is light at around 2-3 mph from the south-southeast. Overall it looks like a nice sunny summer day in NYC.\n"
     ]
    }
   ],
   "source": [
    "for m in convert_to_messages(state[\"values\"][\"messages\"]):\n",
    "    m.pretty_print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify that the original, rolled back run was deleted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original run was correctly deleted\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    await client.runs.get(thread[\"thread_id\"], rolled_back_run[\"run_id\"])\n",
    "except httpx.HTTPStatusError as _:\n",
    "    print(\"Original run was correctly deleted\")"
   ]
  }
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